| Drought monitoring method in Northeast China based on FY-3D/MERSI data |
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| DOI：10.7606/j.issn.1000-7601.2023.04.30 |
| Key Words: FY meteorological satellite remote sensing index drought monitoring radial basis function neural network (RBFNN) models applicability Northeast China |
| Author Name | Affiliation | | WANG Yan | School of Transportation and Geomatics Engineering, Shenyang Jianzhu University, Shenyang, Liaoning 110168, China | | WANG Jingyi | School of Transportation and Geomatics Engineering, Shenyang Jianzhu University, Shenyang, Liaoning 110168, China
Institute of Atmospheric Environment, China Meteorological Administration, Shenyang, Liaoning 110166, China | | FENG Rui | Institute of Atmospheric Environment, China Meteorological Administration, Shenyang, Liaoning 110166, China
Key Laboratory of Agrometeorological Disasters, Liaoning Province, Shenyang, Liaoning 110166, China | | LI Jianing | Liaoning Provincial Meteorological Service Center, Shenyang, Liaoning 110166, China | | WU Jinwen | Institute of Atmospheric Environment, China Meteorological Administration, Shenyang, Liaoning 110166, China
Key Laboratory of Agrometeorological Disasters, Liaoning Province, Shenyang, Liaoning 110166, China | | XU Changhua | Jinzhou Meteorological, Jinzhou, Liaoning 121000, China | | LIN Yi | Liaoning Provincial Meteorological Service Center, Shenyang, Liaoning 110166, China | | JI Ruipeng | Institute of Atmospheric Environment, China Meteorological Administration, Shenyang, Liaoning 110166, China
Key Laboratory of Agrometeorological Disasters, Liaoning Province, Shenyang, Liaoning 110166, China | | YU Wenying | Institute of Atmospheric Environment, China Meteorological Administration, Shenyang, Liaoning 110166, China
Key Laboratory of Agrometeorological Disasters, Liaoning Province, Shenyang, Liaoning 110166, China | | WANG Licheng | School of Transportation and Geomatics Engineering, Shenyang Jianzhu University, Shenyang, Liaoning 110168, China
Institute of Atmospheric Environment, China Meteorological Administration, Shenyang, Liaoning 110166, China |
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| Abstract: |
| Drought is one of the main agrometeorological disasters affecting food security in Northeast China. Remote sensing technology provides a convenient and effective means for large\|scale drought monitoring. In view of the limitations and applicability of remote sensing drought index in drought monitoring during crop growth and development, taking the crop development period of main field crops in Northeast China as the starting point, such as corn and soybean, based on FY-3D/MERSI satellite remote sensing data and ground relative soil moisture measured data, the applicability analysis of drought monitoring index in different crop development stages was carried out. Combined with radial basis function neural network method, the inversion models of relative soil moisture in the whole period and different periods were constructed, and the accuracy verification and comparative analysis were carried out by using the measured relative soil moisture data. The results showed that FY-3D/MERSI data were feasible in drought monitoring, and the apparent thermal inertia was better in low vegetation coverage or bare soil, which was suitable for frozen soil period, bare soil period and sowing~jointing period. The water index was suitable for vegetation growth periods such as sowing~jointing, jointing~tasseling and maturity. The accuracy of the relative soil moisture inversion model in different periods was better than that of the relative soil moisture inversion model in the whole period. The monitoring accuracy was above 80.0%, which was 10%~25% higher than that of the whole period model. Especially in the frozen soil period (March), the inversion accuracy reached 92.6%. According to the difference of crop growth period and morphology, the suitable remote sensing drought index was selected, and the inversion model of relative soil moisture in different periods was established to improve the accuracy and reliability of drought monitoring. |
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